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Computer Science > Machine Learning

Title:Private Causal Inference using Propensity Scores

Abstract: The use of propensity score methods to reduce selection bias when determining causal effects is common practice for observational studies. Although such studies in econometrics, social science, and medicine often rely on sensitive data, there has been no prior work on privatising the propensity scores used to ascertain causal effects from observed data. In this paper, we demonstrate how to privatise the propensity score and quantify how the added noise for privatisation affects the propensity score as well as subsequent causal inference. We test our methods on both simulated and real-world datasets. The results are consistent with our theoretical findings that the privatisation preserves the validity of subsequent causal analysis with high probability. More importantly, our results empirically demonstrate that the proposed solutions are practical for moderately-sized datasets.
Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:1905.12592 [cs.LG]
  (or arXiv:1905.12592v1 [cs.LG] for this version)

Submission history

From: Krikamol Muandet [view email]
[v1] Wed, 29 May 2019 17:11:14 UTC (74 KB)